Overview

Dataset statistics

Number of variables10
Number of observations436
Missing cells8
Missing cells (%)0.2%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory37.2 KiB
Average record size in memory87.3 B

Variable types

Numeric7
Text1
Categorical2

Dataset

Description2023년 지방세, 세외수입 번호판 영치내역을 제공합니다(체납건수, 체납금액, 지방세징수건수, 지방세 징수금액, 영치장소, 자치구명, 세외수입 체납건수, 체납금액, 징수건수, 징수금액).
URLhttps://www.data.go.kr/data/15049787/fileData.do

Alerts

자치구명 has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
체납건수 is highly overall correlated with 체납금액High correlation
체납금액 is highly overall correlated with 체납건수 High correlation
지방세징수건수 is highly overall correlated with 지방세징수금액High correlation
지방세징수금액 is highly overall correlated with 지방세징수건수High correlation
세외체납금액 is highly overall correlated with 징수건수 and 2 other fieldsHigh correlation
징수건수 is highly overall correlated with 세외체납금액 and 2 other fieldsHigh correlation
징수금액 is highly overall correlated with 세외체납금액 and 2 other fieldsHigh correlation
세외건수 is highly overall correlated with 세외체납금액 and 2 other fieldsHigh correlation
세외건수 is highly imbalanced (69.0%)Imbalance
영치장소 has 8 (1.8%) missing valuesMissing
체납건수 has 79 (18.1%) zerosZeros
체납금액 has 79 (18.1%) zerosZeros
지방세징수건수 has 47 (10.8%) zerosZeros
지방세징수금액 has 47 (10.8%) zerosZeros
세외체납금액 has 355 (81.4%) zerosZeros
징수건수 has 355 (81.4%) zerosZeros
징수금액 has 355 (81.4%) zerosZeros

Reproduction

Analysis started2023-12-12 07:44:14.198488
Analysis finished2023-12-12 07:44:20.099846
Duration5.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

체납건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7316514
Minimum0
Maximum61
Zeros79
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:20.456810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile13
Maximum61
Range61
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.6567736
Coefficient of variation (CV)1.5158902
Kurtosis30.665804
Mean3.7316514
Median Absolute Deviation (MAD)1
Skewness4.4135291
Sum1627
Variance31.999088
MonotonicityNot monotonic
2023-12-12T16:44:20.603234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 98
22.5%
2 81
18.6%
0 79
18.1%
3 47
10.8%
5 24
 
5.5%
4 23
 
5.3%
6 16
 
3.7%
7 11
 
2.5%
9 9
 
2.1%
10 8
 
1.8%
Other values (17) 40
9.2%
ValueCountFrequency (%)
0 79
18.1%
1 98
22.5%
2 81
18.6%
3 47
10.8%
4 23
 
5.3%
5 24
 
5.5%
6 16
 
3.7%
7 11
 
2.5%
8 6
 
1.4%
9 9
 
2.1%
ValueCountFrequency (%)
61 1
 
0.2%
41 1
 
0.2%
28 1
 
0.2%
26 2
0.5%
25 3
0.7%
22 2
0.5%
21 1
 
0.2%
19 1
 
0.2%
18 1
 
0.2%
17 2
0.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct309
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530636.67
Minimum0
Maximum10263210
Zeros79
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:20.787646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1105885
median267500
Q3637345
95-th percentile1839085
Maximum10263210
Range10263210
Interquartile range (IQR)531460

Descriptive statistics

Standard deviation872055.96
Coefficient of variation (CV)1.6434144
Kurtosis44.389601
Mean530636.67
Median Absolute Deviation (MAD)230725
Skewness5.3632378
Sum2.3135759 × 108
Variance7.6048159 × 1011
MonotonicityNot monotonic
2023-12-12T16:44:20.961899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
18.1%
133750 9
 
2.1%
267500 6
 
1.4%
133550 4
 
0.9%
149100 4
 
0.9%
74550 4
 
0.9%
133280 4
 
0.9%
278570 3
 
0.7%
355620 3
 
0.7%
267100 3
 
0.7%
Other values (299) 317
72.7%
ValueCountFrequency (%)
0 79
18.1%
7300 1
 
0.2%
28310 1
 
0.2%
44480 1
 
0.2%
45430 1
 
0.2%
51450 1
 
0.2%
56190 1
 
0.2%
61550 1
 
0.2%
66020 1
 
0.2%
74550 4
 
0.9%
ValueCountFrequency (%)
10263210 1
0.2%
6603580 1
0.2%
5162000 2
0.5%
3628610 1
0.2%
3512440 1
0.2%
3438890 1
0.2%
3344580 1
0.2%
3031340 1
0.2%
2958860 1
0.2%
2597400 1
0.2%

지방세징수건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5917431
Minimum0
Maximum44
Zeros47
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:21.127304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile12
Maximum44
Range44
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3075262
Coefficient of variation (CV)0.93810262
Kurtosis19.481171
Mean4.5917431
Median Absolute Deviation (MAD)2
Skewness3.2095831
Sum2002
Variance18.554782
MonotonicityNot monotonic
2023-12-12T16:44:21.269019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 88
20.2%
2 69
15.8%
4 52
11.9%
0 47
10.8%
5 37
8.5%
6 36
8.3%
7 25
 
5.7%
1 16
 
3.7%
8 14
 
3.2%
9 14
 
3.2%
Other values (13) 38
8.7%
ValueCountFrequency (%)
0 47
10.8%
1 16
 
3.7%
2 69
15.8%
3 88
20.2%
4 52
11.9%
5 37
8.5%
6 36
8.3%
7 25
 
5.7%
8 14
 
3.2%
9 14
 
3.2%
ValueCountFrequency (%)
44 1
 
0.2%
28 1
 
0.2%
23 1
 
0.2%
22 1
 
0.2%
21 2
 
0.5%
19 2
 
0.5%
18 1
 
0.2%
15 2
 
0.5%
14 2
 
0.5%
13 5
1.1%

지방세징수금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct357
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594317.39
Minimum0
Maximum3895210
Zeros47
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:21.422349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1266560
median477125
Q3775345
95-th percentile1614262.5
Maximum3895210
Range3895210
Interquartile range (IQR)508785

Descriptive statistics

Standard deviation549180.89
Coefficient of variation (CV)0.92405322
Kurtosis6.8516101
Mean594317.39
Median Absolute Deviation (MAD)259390
Skewness2.1037816
Sum2.5912238 × 108
Variance3.0159965 × 1011
MonotonicityNot monotonic
2023-12-12T16:44:21.596539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
10.8%
401250 5
 
1.1%
300000 3
 
0.7%
535000 3
 
0.7%
223650 3
 
0.7%
266560 3
 
0.7%
614330 3
 
0.7%
505860 3
 
0.7%
533430 3
 
0.7%
334320 3
 
0.7%
Other values (347) 360
82.6%
ValueCountFrequency (%)
0 47
10.8%
30 1
 
0.2%
1530 1
 
0.2%
3520 1
 
0.2%
8670 1
 
0.2%
10170 1
 
0.2%
16070 1
 
0.2%
19500 1
 
0.2%
20590 1
 
0.2%
27950 1
 
0.2%
ValueCountFrequency (%)
3895210 1
0.2%
3376010 1
0.2%
3370870 1
0.2%
2945580 1
0.2%
2436110 2
0.5%
2423600 1
0.2%
2341850 1
0.2%
2185680 1
0.2%
2151650 1
0.2%
2140650 1
0.2%

영치장소
Text

MISSING 

Distinct389
Distinct (%)90.9%
Missing8
Missing (%)1.8%
Memory size3.5 KiB
2023-12-12T16:44:21.886820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length37
Mean length20.303738
Min length4

Characters and Unicode

Total characters8690
Distinct characters306
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367 ?
Unique (%)85.7%

Sample

1st row성남시 중원구 상대원1동 378-51, 현대그린맨션 앞
2nd row가덕면 상장인차로 242
3rd row낙영로40번길 4
4th row청주시상당구 남일면 단재로466 도로가
5th row청주시상당구 용담.명암.산성동 주공아파트
ValueCountFrequency (%)
청주시청원구 107
 
6.7%
청주시흥덕구 106
 
6.6%
청주시서원구 100
 
6.3%
청주시상당구 52
 
3.3%
율량.사천동 40
 
2.5%
주차장 35
 
2.2%
오창읍 28
 
1.8%
용암1동 26
 
1.6%
앞 24
 
1.5%
내수읍 16
 
1.0%
Other values (677) 1065
66.6%
2023-12-12T16:44:22.358971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1395
 
16.1%
청 526
 
6.1%
주 482
 
5.5%
구 400
 
4.6%
시 386
 
4.4%
동 336
 
3.9%
1 316
 
3.6%
원 234
 
2.7%
2 228
 
2.6%
로 146
 
1.7%
Other values (296) 4241
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5607
64.5%
Decimal Number 1435
 
16.5%
Space Separator 1395
 
16.1%
Dash Punctuation 117
 
1.3%
Other Punctuation 86
 
1.0%
Lowercase Letter 38
 
0.4%
Uppercase Letter 8
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 526
 
9.4%
주 482
 
8.6%
구 400
 
7.1%
시 386
 
6.9%
동 336
 
6.0%
원 234
 
4.2%
로 146
 
2.6%
덕 145
 
2.6%
흥 116
 
2.1%
서 107
 
1.9%
Other values (256) 2729
48.7%
Lowercase Letter
ValueCountFrequency (%)
l 12
31.6%
h 9
23.7%
a 2
 
5.3%
b 2
 
5.3%
c 2
 
5.3%
i 2
 
5.3%
u 1
 
2.6%
n 1
 
2.6%
k 1
 
2.6%
t 1
 
2.6%
Other values (5) 5
13.2%
Decimal Number
ValueCountFrequency (%)
1 316
22.0%
2 228
15.9%
5 140
9.8%
3 138
9.6%
0 119
 
8.3%
7 109
 
7.6%
4 107
 
7.5%
8 106
 
7.4%
6 92
 
6.4%
9 80
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
R 1
12.5%
K 1
12.5%
T 1
12.5%
P 1
12.5%
A 1
12.5%
B 1
12.5%
H 1
12.5%
L 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 81
94.2%
, 3
 
3.5%
/ 2
 
2.3%
Space Separator
ValueCountFrequency (%)
1395
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5607
64.5%
Common 3037
34.9%
Latin 46
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 526
 
9.4%
주 482
 
8.6%
구 400
 
7.1%
시 386
 
6.9%
동 336
 
6.0%
원 234
 
4.2%
로 146
 
2.6%
덕 145
 
2.6%
흥 116
 
2.1%
서 107
 
1.9%
Other values (256) 2729
48.7%
Latin
ValueCountFrequency (%)
l 12
26.1%
h 9
19.6%
a 2
 
4.3%
b 2
 
4.3%
c 2
 
4.3%
i 2
 
4.3%
u 1
 
2.2%
n 1
 
2.2%
k 1
 
2.2%
t 1
 
2.2%
Other values (13) 13
28.3%
Common
ValueCountFrequency (%)
1395
45.9%
1 316
 
10.4%
2 228
 
7.5%
5 140
 
4.6%
3 138
 
4.5%
0 119
 
3.9%
- 117
 
3.9%
7 109
 
3.6%
4 107
 
3.5%
8 106
 
3.5%
Other values (7) 262
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5607
64.5%
ASCII 3083
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1395
45.2%
1 316
 
10.2%
2 228
 
7.4%
5 140
 
4.5%
3 138
 
4.5%
0 119
 
3.9%
- 117
 
3.8%
7 109
 
3.5%
4 107
 
3.5%
8 106
 
3.4%
Other values (30) 308
 
10.0%
Hangul
ValueCountFrequency (%)
청 526
 
9.4%
주 482
 
8.6%
구 400
 
7.1%
시 386
 
6.9%
동 336
 
6.0%
원 234
 
4.2%
로 146
 
2.6%
덕 145
 
2.6%
흥 116
 
2.1%
서 107
 
1.9%
Other values (256) 2729
48.7%

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
충청북도 청주시
436 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도 청주시
2nd row충청북도 청주시
3rd row충청북도 청주시
4th row충청북도 청주시
5th row충청북도 청주시

Common Values

ValueCountFrequency (%)
충청북도 청주시 436
100.0%

Length

2023-12-12T16:44:22.499863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:44:22.602784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 436
50.0%
청주시 436
50.0%

세외건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct31
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
0
355 
2
 
9
1
 
7
4
 
6
5
 
6
Other values (26)
53 

Length

Max length7
Median length1
Mean length1.0963303
Min length1

Unique

Unique12 ?
Unique (%)2.8%

Sample

1st row10
2nd row4
3rd row6
4th row3
5th row32

Common Values

ValueCountFrequency (%)
0 355
81.4%
2 9
 
2.1%
1 7
 
1.6%
4 6
 
1.4%
5 6
 
1.4%
3 5
 
1.1%
38 4
 
0.9%
7 4
 
0.9%
10 4
 
0.9%
33 3
 
0.7%
Other values (21) 33
 
7.6%

Length

2023-12-12T16:44:22.714792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 355
81.6%
2 9
 
2.1%
1 7
 
1.6%
4 6
 
1.4%
5 6
 
1.4%
3 5
 
1.1%
38 4
 
0.9%
7 4
 
0.9%
10 4
 
0.9%
33 3
 
0.7%
Other values (20) 32
 
7.4%

세외체납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean377482.27
Minimum0
Maximum9832000
Zeros355
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:22.833013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2383500
Maximum9832000
Range9832000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1177205.7
Coefficient of variation (CV)3.118572
Kurtosis28.709512
Mean377482.27
Median Absolute Deviation (MAD)0
Skewness4.8852836
Sum1.6458227 × 108
Variance1.3858131 × 1012
MonotonicityNot monotonic
2023-12-12T16:44:22.981145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 355
81.4%
1056600 2
 
0.5%
1391520 2
 
0.5%
1566860 1
 
0.2%
3463000 1
 
0.2%
4422050 1
 
0.2%
3059800 1
 
0.2%
2647190 1
 
0.2%
2172630 1
 
0.2%
1022000 1
 
0.2%
Other values (70) 70
 
16.1%
ValueCountFrequency (%)
0 355
81.4%
348600 1
 
0.2%
364360 1
 
0.2%
399000 1
 
0.2%
424760 1
 
0.2%
431000 1
 
0.2%
431010 1
 
0.2%
449440 1
 
0.2%
452000 1
 
0.2%
480670 1
 
0.2%
ValueCountFrequency (%)
9832000 1
0.2%
9278730 1
0.2%
8488460 1
0.2%
8207570 1
0.2%
6409340 1
0.2%
4868000 1
0.2%
4836000 1
0.2%
4674350 1
0.2%
4530000 1
0.2%
4422050 1
0.2%

징수건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.087156
Minimum0
Maximum28
Zeros355
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:23.094595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.25
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.353738
Coefficient of variation (CV)3.084873
Kurtosis21.386936
Mean1.087156
Median Absolute Deviation (MAD)0
Skewness4.3207105
Sum474
Variance11.247559
MonotonicityNot monotonic
2023-12-12T16:44:23.210380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 355
81.4%
1 16
 
3.7%
3 12
 
2.8%
2 12
 
2.8%
5 7
 
1.6%
4 6
 
1.4%
6 4
 
0.9%
15 4
 
0.9%
10 4
 
0.9%
8 4
 
0.9%
Other values (8) 12
 
2.8%
ValueCountFrequency (%)
0 355
81.4%
1 16
 
3.7%
2 12
 
2.8%
3 12
 
2.8%
4 6
 
1.4%
5 7
 
1.6%
6 4
 
0.9%
7 2
 
0.5%
8 4
 
0.9%
9 2
 
0.5%
ValueCountFrequency (%)
28 1
 
0.2%
21 1
 
0.2%
19 2
0.5%
18 2
0.5%
17 1
 
0.2%
15 4
0.9%
11 1
 
0.2%
10 4
0.9%
9 2
0.5%
8 4
0.9%

징수금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175252.61
Minimum0
Maximum4714520
Zeros355
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-12T16:44:23.341647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1107250
Maximum4714520
Range4714520
Interquartile range (IQR)0

Descriptive statistics

Standard deviation500261.84
Coefficient of variation (CV)2.8545185
Kurtosis32.024324
Mean175252.61
Median Absolute Deviation (MAD)0
Skewness4.8048291
Sum76410140
Variance2.5026191 × 1011
MonotonicityNot monotonic
2023-12-12T16:44:23.507571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 355
81.4%
500000 7
 
1.6%
1000000 5
 
1.1%
200000 3
 
0.7%
1391520 2
 
0.5%
300000 2
 
0.5%
1200000 2
 
0.5%
600000 2
 
0.5%
1103000 1
 
0.2%
2004730 1
 
0.2%
Other values (56) 56
 
12.8%
ValueCountFrequency (%)
0 355
81.4%
170000 1
 
0.2%
200000 3
 
0.7%
261190 1
 
0.2%
290000 1
 
0.2%
300000 2
 
0.5%
310000 1
 
0.2%
340000 1
 
0.2%
348600 1
 
0.2%
364360 1
 
0.2%
ValueCountFrequency (%)
4714520 1
0.2%
4530000 1
0.2%
2870000 1
0.2%
2600000 1
0.2%
2172630 1
0.2%
2004730 1
0.2%
1787400 1
0.2%
1590000 1
0.2%
1531760 1
0.2%
1520000 1
0.2%

Interactions

2023-12-12T16:44:18.998159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:14.715225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.543363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.266510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.887712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.628829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.281170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.104479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:14.846372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.646763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.356070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.980670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.710842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.372263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.211524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:14.974622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.737803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.457569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.086500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.789370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.496258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.332899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.095433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.865136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.552519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.189251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.889016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.579554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.466953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.212683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.976627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.645774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.317754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.983649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.685671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.580380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.344248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.088972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.739362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.443858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.090068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.791934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:19.677059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:15.454723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.183353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:16.809309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:17.549392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.185194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:18.893344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:44:23.627795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액지방세징수건수지방세징수금액세외건수세외체납금액징수건수징수금액
체납건수1.0000.9720.1420.2160.4190.2140.0000.117
체납금액0.9721.0000.1500.1470.3460.2200.0000.082
지방세징수건수0.1420.1501.0000.7510.7210.0000.0000.000
지방세징수금액0.2160.1470.7511.0000.5350.3080.0000.000
세외건수0.4190.3460.7210.5351.0000.9600.9670.956
세외체납금액0.2140.2200.0000.3080.9601.0000.8020.884
징수건수0.0000.0000.0000.0000.9670.8021.0000.913
징수금액0.1170.0820.0000.0000.9560.8840.9131.000
2023-12-12T16:44:23.766965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액지방세징수건수지방세징수금액세외체납금액징수건수징수금액세외건수
체납건수1.0000.941-0.175-0.222-0.004-0.009-0.0020.183
체납금액0.9411.000-0.212-0.148-0.016-0.025-0.0140.147
지방세징수건수-0.175-0.2121.0000.8060.1330.1310.1240.368
지방세징수금액-0.222-0.1480.8061.0000.0850.0820.0840.213
세외체납금액-0.004-0.0160.1330.0851.0000.9900.9910.775
징수건수-0.009-0.0250.1310.0820.9901.0000.9930.799
징수금액-0.002-0.0140.1240.0840.9910.9931.0000.760
세외건수0.1830.1470.3680.2130.7750.7990.7601.000

Missing values

2023-12-12T16:44:19.826538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:44:20.025197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

체납건수체납금액지방세징수건수지방세징수금액영치장소자치구명세외건수세외체납금액징수건수징수금액
06151053000성남시 중원구 상대원1동 378-51, 현대그린맨션 앞충청북도 청주시10156686031103000
113186532000가덕면 상장인차로 242충청북도 청주시4125892041787400
211602704747880낙영로40번길 4충청북도 청주시64494406449440
3006922870청주시상당구 남일면 단재로466 도로가충청북도 청주시3105992031059000
4004533430청주시상당구 용담.명암.산성동 주공아파트충청북도 청주시322453240172870000
5006642700청주시상당구 용담.명암.산성동 주공아파트충청북도 청주시388207570151200000
62279940355840영운동 신라송림 101동 5-6라인 앞충청북도 청주시1415270109392930
7004795840청주시상당구 용암1동 월평로144번길 21충청북도 청주시1105660011056600
811332803399840청주시상당구 용암1동 부영아파트주차장충청북도 청주시2127260021272600
911332802266560용암동 부영아파트 106동 앞충청북도 청주시353103560101000000
체납건수체납금액지방세징수건수지방세징수금액영치장소자치구명세외건수세외체납금액징수건수징수금액
426222597400211332400청주시청원구 내수읍 내수주공아파트 102동 주차장충청북도 청주시0000
42711669702350630청주시청원구 오창읍 오창 부영5단지아파트 지하주차장충청북도 청주시0000
42811401103265710청주시청원구 내덕1동 상당로 290-7충청북도 청주시0000
4291997605588490청주시청원구 율량.사천동 1순환로225번길62 맞은편충청북도 청주시0000
43011640409470750청주시청원구 율량.사천동 안덕벌로57번길 32-12 행복한빌충청북도 청주시0000
4312140200132790징수촉탁해제충청북도 청주시0000
432004419270청주시청원구 오창읍 용의한수 맞은편 길목 주차충청북도 청주시0000
43311115203398460청주시청원구 내덕1동 안덕벌로52번길 52충청북도 청주시0000
43411406803353480청주시청원구 내수읍 내수주공102동앞주차장충청북도 청주시0000
43522675003314630청주시청원구 우암동 우암동 대성로268번길34충청북도 청주시0000

Duplicate rows

Most frequently occurring

체납건수체납금액지방세징수건수지방세징수금액영치장소자치구명세외건수세외체납금액징수건수징수금액# duplicates
02278570122436110청주시청원구 내덕2동 공항로58번길 15충청북도 청주시00002